Assessment of Global Forest Coverage through Machine Learning Algorithms


  • P S Metkewar Symbiosis International University image/svg+xml
  • Ravi Chauhan Symbiosis International University image/svg+xml
  • A Prasanth Sri Venkateswara College of Engineering
  • Malathy Sathyamoorthy KPR Institute of Engineering and Technology image/svg+xml



Forest Coverage, Deforestation, Remote Sensing, Ground Surveys, Environmental Issues, Climate Change, Machine Learning, Mean Squared error, R2 Score, Mean Absolute Error, Root Mean Square Error


This exploration of paper presents an investigation of the Forest Region Inclusion Dataset that gives data on the backwoods inclusion of different nations overall from 1990 to 2020. The dataset contains country-wise information on population, population density, population development rate, total population rate, and forest region inclusion. We examined this dataset to decide the patterns in woodland region inclusion across various nations and mainlands, as well as the connection among populace and backwoods region inclusion. Our discoveries show that while certain nations have essentially expanded their forest region inclusion, others have encountered a decline. Besides, we found that population density and development rate are adversely related with forest area coverage. Authors have implemented four machine learning algorithms that are Linear Regression, Decision Tree, Random Forest and Support Vector Machine on the dataset.


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How to Cite

Metkewar PS, Chauhan R, Prasanth A, Sathyamoorthy M. Assessment of Global Forest Coverage through Machine Learning Algorithms . EAI Endorsed Scal Inf Syst [Internet]. 2024 Feb. 15 [cited 2024 Jul. 22];11(4). Available from: